1 Dept. Of Industrial and Production Engineering, K.L.College of Engineering, Vaddeswaram, Guntur(Dist), Andhra Pradesh, INDIA-522502.
2 School of Mechanical Engineering, Kyungpook National University, Daegu, South Korea.
* Corresponding Author.
This paper proposes a neural network-based optimization scheme for predicting localized stable cutting states in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces and critical chatter locations are predicted as a function of operating variables including tool–overhang length. A neural network model is employed to develop the generalized relations. The optimum cutting parameters are predicted from the model with the help of binary-coded Genetic Algorithms. Results are illustrated with the data of four different work materials.
Critical chatter length, Tool overhang, Neural networks, Optimum parameters, Orthogonal turning
